Development of bacteria-based bioassays for arsenic detection in natural waters.

Arsenic contamination of natural waters is a worldwide concern, as the drinking water supplies for large populations can have high concentrations of arsenic. Traditional techniques to detect arsenic in natural water samples can be costly and time-consuming; therefore, robust and inexpensive methods to detect arsenic in water are highly desirable. Additionally, methods for detecting arsenic in the field have been greatly sought after. This article focuses on the use of bacteria-based assays as an emerging method that is both robust and inexpensive for the detection of arsenic in groundwater both in the field and in the laboratory. The arsenic detection elements in bacteria-based bioassays are biosensor-reporter strains; genetically modified strains of, e.g., Escherichia coli, Bacillus subtilis, Staphylococcus aureus, and Rhodopseudomonas palustris. In response to the presence of arsenic, such bacteria produce a reporter protein, the amount or activity of which is measured in the bioassay. Some of these bacterial biosensor-reporters have been successfully utilized for comparative in-field analyses through the use of simple solution-based assays, but future methods may concentrate on miniaturization using fiberoptics or microfluidics platforms. Additionally, there are other potential emerging bioassays for the detection of arsenic in natural waters including nematodes and clams.

Robust fault detection using consistency techniques for uncertainty handling

Often practical performance of analytical redundancy for fault detection and diagnosis is decreased by uncertainties prevailing not only in the system model, but also in the measurements. In this paper, the problem of fault detection is stated as a constraint satisfaction problem over continuous domains with a big number of variables and constraints. This problem can be solved using modal interval analysis and consistency techniques. Consistency techniques are then shown to be particularly efficient to check the consistency of the analytical redundancy relations (ARRs), dealing with uncertain measurements and parameters. Through the work presented in this paper, it can be observed that consistency techniques can be used to increase the performance of a robust fault detection tool, which is based on interval arithmetic. The proposed method is illustrated using a nonlinear dynamic model of a hydraulic system

Kernel-based methods for change detection in remote sensing images

Nowadays, the joint exploitation of images acquired daily by remote sensing instruments and of images available from archives allows a detailed monitoring of the transitions occurring at the surface of the Earth. These modifications of the land cover generate spectral discrepancies that can be detected via the analysis of remote sensing images. Independently from the origin of the images and of type of surface change, a correct processing of such data implies the adoption of flexible, robust and possibly nonlinear method, to correctly account for the complex statistical relationships characterizing the pixels of the images. This Thesis deals with the development and the application of advanced statistical methods for multi-temporal optical remote sensing image processing tasks. Three different families of machine learning models have been explored and fundamental solutions for change detection problems are provided. In the first part, change detection with user supervision has been considered. In a first application, a nonlinear classifier has been applied with the intent of precisely delineating flooded regions from a pair of images. In a second case study, the spatial context of each pixel has been injected into another nonlinear classifier to obtain a precise mapping of new urban structures. In both cases, the user provides the classifier with examples of what he believes has changed or not. In the second part, a completely automatic and unsupervised method for precise binary detection of changes has been proposed. The technique allows a very accurate mapping without any user intervention, resulting particularly useful when readiness and reaction times of the system are a crucial constraint. In the third, the problem of statistical distributions shifting between acquisitions is studied. Two approaches to transform the couple of bi-temporal images and reduce their differences unrelated to changes in land cover are studied. The methods align the distributions of the images, so that the pixel-wise comparison could be carried out with higher accuracy. Furthermore, the second method can deal with images from different sensors, no matter the dimensionality of the data nor the spectral information content. This opens the doors to possible solutions for a crucial problem in the field: detecting changes when the images have been acquired by two different sensors.

A new, automated, four-colour interphase FISH approach for the simultaneous detection of specific aneuploidies of diagnostic and prognostic significance in high hyperdiploid ALL

In hyperdiploid acute lymphoblastic leukaemia (ALL), the simultaneous occurrence of specific aneuploidies confers a more favourable outcome than hyperdiploidy alone. Interphase (I) FISH complements conventional cytogenetics (CC) through its sensitivity and ability to detect chromosome aberrations in non-dividing cells. To overcome the limits of manual I-FISH, we developed an automated four-colour I-FISH approach and assessed its ability to detect concurrent aneuploidies in ALL. I-FISH was performed using centromeric probes for chromosomes 4, 6, 10 and 17. Parameters established for automatic nucleus selection and signal detection were evaluated (3 controls). Cut-off values were determined (10 controls, 1000 nuclei/case). Combinations of aneuploidies were considered relevant when each aneuploidy was individually significant. Results obtained in 10 ALL patients (1500 nuclei/patient) were compared with those by CC. Various combinations of aneuploidies were identified. All clones detected by CC were observed by I-FISH. I-FISH revealed numerous additional abnormal clones, ranging between 0.1% and 31.6%, based on the large number of nuclei evaluated. Four-colour automated I-FISH permits the identification of concurrent aneuploidies of prognostic significance in hyperdiploid ALL. Large numbers of cells can be analysed rapidly by this method. Owing to its high sensitivity, the method provides a powerful tool for the detection of small abnormal clones at diagnosis and during follow up. Compared to CC, it generates a more detailed cytogenetic picture, the biological and clinical significance of which merits further evaluation. Once optimised for a given set of probes, the system can be easily adapted for other probe combinations.

Multisensor change detection with nonlinear canonical correlation

The analysis of multi-modal and multi-sensor images is nowadays of paramount importance for Earth Observation (EO) applications. There exist a variety of methods that aim at fusing the different sources of information to obtain a compact representation of such datasets. However, for change detection existing methods are often unable to deal with heterogeneous image sources and very few consider possible nonlinearities in the data. Additionally, the availability of labeled information is very limited in change detection applications. For these reasons, we present the use of a semi-supervised kernel-based feature extraction technique. It incorporates a manifold regularization accounting for the geometric distribution and jointly addressing the small sample problem. An exhaustive example using Landsat 5 data illustrates the potential of the method for multi-sensor change detection.

Multi-scale label interaction to improve object detection and segmentation

The project aims at advancing the state of the art in the use of context information for classification of image and video data. The use of context in the classification of images has been showed of great importance to improve the performance of actual object recognition systems. In our project we proposed the concept of Multi-scale Feature Labels as a general and compact method to exploit the local and global context. The feature extraction from the discriminative probability or classification confidence label field is of great novelty. Moreover the use of a multi-scale representation of the feature labels lead to a compact and efficient description of the context. The goal of the project has been also to provide a general-purpose method and prove its suitability in different image/video analysis problem. The two-year project generated 5 journal publications (plus 2 under submission), 10 conference publications (plus 2 under submission) and one patent (plus 1 pending). Of these publications, a relevant number make use of the main result of this project to improve the results in detection and/or segmentation of objects.

Direct detection of a BRAF mutation in total RNA from melanoma cells using cantilever arrays.

Malignant melanoma, the deadliest form of skin cancer, is characterized by a predominant mutation in the BRAF gene. Drugs that target tumours carrying this mutation have recently entered the clinic. Accordingly, patients are routinely screened for mutations in this gene to determine whether they can benefit from this type of treatment. The current gold standard for mutation screening uses real-time polymerase chain reaction and sequencing methods. Here we show that an assay based on microcantilever arrays can detect the mutation nanomechanically without amplification in total RNA samples isolated from melanoma cells. The assay is based on a BRAF-specific oligonucleotide probe. We detected mutant BRAF at a concentration of 500 pM in a 50-fold excess of the wild-type sequence. The method was able to distinguish melanoma cells carrying the mutation from wild-type cells using as little as 20 ng µl(-1) of RNA material, without prior PCR amplification and use of labels.

Detection of four Plasmodium species in blood from humans by 18S rRNA gene subunit-based and specific real-time PCR assays

Résumé : Un nombre croissant de cas de malaria chez les voyageurs et migrants a été rapporté. Bien que l'analyse microscopique des frottis sanguins reste traditionnellement l'outil diagnostic de référence, sa fiabilité dépend considérablement de l'expertise de l'examinateur, pouvant elle-même faire défaut sous nos latitudes. Une PCR multiplex en temps réel a donc été développée en vue d'une standardisation du diagnostic. Un ensemble d'amorces génériques ciblant une région hautement conservée du gène d'ARN ribosomial 18S du genre Plasmodium a tout d'abord été conçu, dont le polymorphisme du produit d'amplification semblait suffisant pour créer quatre sondes spécifiques à l'espèce P. falciparum, P. malariae, P. vivax et P. ovale. Ces sondes utilisées en PCR en temps réel se sont révélées capables de détecter une seule copie de plasmide de P. falciparum, P. malariae, P. vivax et P. ovale spécifiquement. La même sensibilité a été obtenue avec une sonde de screening pouvant détecter les quatre espèces. Quatre-vingt-dix-sept échantillons de sang ont ensuite été testés, dont on a comparé la microscopie et la PCR en temps réel pour 66 (60 patients) d'entre eux. Ces deux méthodes ont montré une concordance globale de 86% pour la détection de plasmodia. Les résultats discordants ont été réévalués grâce à des données cliniques, une deuxième expertise microscopique et moléculaire (laboratoire de Genève et de l'Institut Suisse Tropical de Bâle), ainsi qu'à l'aide du séquençage. Cette nouvelle analyse s'est prononcé en faveur de la méthode moléculaire pour tous les neuf résultats discordants. Sur les 31 résultats positifs par les deux méthodes, la même réévaluation a pu donner raison 8 fois sur 9 à la PCR en temps réel sur le plan de l'identification de l'espèce plasmodiale. Les 31 autres échantillons ont été analysés pour le suivi de sept patients sous traitement antimalarique. Il a été observé une baisse rapide du nombre de parasites mesurée par la PCR en temps réel chez six des sept patients, baisse correspondant à la parasitémie déterminée microscopiquement. Ceci suggère ainsi le rôle potentiel de la PCR en temps réel dans le suivi thérapeutique des patients traités par antipaludéens. Abstract : There have been reports of increasing numbers of cases of malaria among migrants and travelers. Although microscopic examination of blood smears remains the "gold standard" in diagnosis, this method suffers from insufficient sensitivity and requires considerable expertise. To improve diagnosis, a multiplex real-time PCR was developed. One set of generic primers targeting a highly conserved region of the 18S rRNA gene of the genus Plasmodium was designed; the primer set was polymorphic enough internally to design four species-specific probes for P. falciparum, P. vivax, P. malarie, and P. ovale. Real-time PCR with species-specific probes detected one plasmid copy of P. falciparum, P. vivax, P. malariae, and P. ovale specifically. The same sensitivity was achieved for all species with real-time PCR with the 18S screening probe. Ninety-seven blood samples were investigated. For 66 of them (60 patients), microscopy and real-time PCR results were compared and had a crude agreement of 86% for the detection of plasmodia. Discordant results were reevaluated with clinical, molecular, and sequencing data to resolve them. All nine discordances between 18S screening PCR and microscopy were resolved in favor of the molecular method, as were eight of nine discordances at the species level for the species-specific PCR among the 31 samples positive by both methods. The other 31 blood samples were tested to monitor the antimalaria treatment in seven patients. The number of parasites measured by real-time PCR fell rapidly for six out of seven patients in parallel to parasitemia determined microscopically. This suggests a role of quantitative PCR for the monitoring of patients receiving antimalaria therapy.

Postoperative delirium. Part 2: detection, prevention and treatment.

To target pharmacological prevention, instruments giving an approximation of an individual patient's risk of developing postoperative delirium are available. In view of the variable clinical presentation, identifying patients in whom prophylaxis has failed (that is, who develop delirium) remains a challenge. Several bedside instruments are available for the routine ward and ICU setting. Several have been shown to have a high specificity and sensitivity when compared with the standard definitions according to DSM-IV-TR and ICD-10. The Confusion Assessment Method (CAM) and a version specifically developed for the intensive care setting (CAM-ICU) have emerged as a standard. However, alternatives allowing grading of the severity of delirium are also available. In many units, the approach to delirium follows a three-step strategy. Initially, non-pharmacological multicomponent strategies are used for primary prevention. As a second step, pharmacological prophylaxis may be added. Perioperative administration of haloperidol has been shown to reduce the severity, but not the incidence, of delirium. Perioperative administration of atypical antipsychotics has been shown to reduce the incidence of delirium in specific groups of patients. In patients with delirium, both symptomatic and causal treatment of delirium need to be considered. So far symptomatic treatment of delirium is primarily based on antipsychotics. Currently, cholinesterase inhibitors cannot be recommended and the data on dexmedetomidine are inconclusive. With the exception of alcohol-withdrawal delirium, there is no role for benzodiazepines in the treatment of delirium. It is unclear whether treating delirium prevents long-term sequelae.

Enhanced sensitivity detection of protein immobilization by fluorescent interference on oxidized silicon.

We present a silicon chip-based approach for the enhanced sensitivity detection of surface-immobilized fluorescent molecules. Green fluorescent protein (GFP) is bound to the silicon substrate by a disuccinimidyl terephtalate-aminosilane immobilization procedure. The immobilized organic layers are characterized by surface analysis techniques, like ellipsometry, atomic force microscopy (AFM) and X-ray induced photoelectron spectroscopy. We obtain a 20-fold enhancement of the fluorescent signal, using constructive interference effects in a fused silica dielectric layer, deposited before immobilization onto the silicon. Our method opens perspectives to increase by an order of magnitude the fluorescent response of surface immobilized DNA- or protein-based layers for a variety of biosensor applications.

Front-Crawl propulsive phase detection using inertial sensors

Front crawl is an alternating swimming stroke technique in which different phases of arm movement induce changes in acceleration of limbs and body. This study proposes a new approach to use inertial body worn sensors to estimate main temporal phases of front crawl. Distinctive features in kinematic signals are used to detect the temporal phases. These temporal phases are key information sources of qualitative and quantitative evaluation of swimming coordination, which have been assessed previously by video analysis. The present method has been evaluated upon a wide range of coordination and showed a difference of 4.9% with video based system. The results are in line with video analysis inter-operator variability yet offering an easy-to-use system for trainers.

Self-paced Movement Intention Detection from Human Brain Signals: Invasive and Non-invasive EEG

Neural signatures of humans' movement intention can be exploited by future neuroprosthesis. We propose a method for detecting self-paced upper limb movement intention from brain signals acquired with both invasive and noninvasive methods. In the first study with scalp electroencephalograph (EEG) signals from healthy controls, we report single trial detection of movement intention using movement related potentials (MRPs) in a frequency range between 0.1 to 1 Hz. Movement intention can be detected above chance level (p<0.05) on average 460 ms before the movement onset with low detection rate during the on-movement intention period. Using intracranial EEG (iEEG) from one epileptic subject, we detect movement intention as early as 1500 ms before movement onset with accuracy above 90% using electrodes implanted in the bilateral supplementary motor area (SMA). The coherent results obtained with non-invasive and invasive method and its generalization capabilities across different days of recording, strengthened the theory that self-paced movement intention can be detected before movement initiation for the advancement in robot-assisted neurorehabilitation.

Simple and fast methods based on solid-phase extraction coupled to liquid chromatography with UV detection for the monitoring of caffeine in natural and waste waters as marker of anthropogenic impact

Two concentration methods for fast and routine determination of caffeine (using HPLC-UV detection) in surface, and wastewater are evaluated. Both methods are based on solid-phase extraction (SPE) concentration with octadecyl silica sorbents. A common “offline” SPE procedure shows that quantitative recovery of caffeine is obtained with 2 mL of an elution mixture solvent methanol-water containing at least 60% methanol. The method detection limit is 0.1 μg L−1 when percolating 1 L samples through the cartridge. The development of an “online” SPE method based on a mini-SPE column, containing 100 mg of the same sorbent, directly connected to the HPLC system allows the method detection limit to be decreased to 10 ng L−1 with a sample volume of 100 mL. The “offline” SPE method is applied to the analysis of caffeine in wastewater samples, whereas the “on-line” method is used for analysis in natural waters from streams receiving significant water intakes from local wastewater treatment plants

Small scale underwater change detection

In this paper, we present a method to deal with the constraints of the underwater medium for finding changes between sequences of underwater images. One of the main problems of underwater medium for automatically detecting changes is the low altitude of the camera when taking pictures. This emphasise the parallax effect between the images as they are not taken exactly at the same position. In order to solve this problem, we are geometrically registering the images together taking into account the relief of the scene

Detection of moving groups among early type stars

Our procedure to detect moving groups in the solar neighbourhood (Chen et al., 1997) in the four-dimensional space of the stellar velocity components and age has been improved. The method, which takes advantadge of non-parametric estimators of density distribution to avoid any a priori knowledge of the kinematic properties of these stellar groups, now includes the effect of observational errors on the process to select moving group stars, uses a better estimation of the density distribution of the total sample and field stars, and classifies moving group stars using all the available information. It is applied here to an accurately selected sample of early-type stars with known radial velocities and Strömgren photometry. Astrometric data are taken from the HIPPARCOS catalogue (ESA, 1997), which results in an important decrease in the observational errors with respect to ground-based data, and ensures the uniformity of the observed data. Both the improvement of our method and the use of precise astrometric data have allowed us not only to confirm the existence of classical moving groups, but also to detect finer structures that in several cases can be related to kinematic properties of nearby open clusters or associations.